Although the chemistry of phosphorus and nitrogen has fascinated chemists for more than 350 years, the Hückel aromatic cyclotriphosphazene (P3N3, 2) molecule-a key molecular building block in phosphorus chemistry-has remained elusive. Here, we report a facile, versatile pathway producing cyclotriphosphazene and its Dewar benzene-type isomer (P3N3, 5) in ammonia-phosphine ices at 5 K exposed to ionizing radiation. Both isomers were detected in the gas phase upon sublimation via photoionization reflectron time-of-flight mass spectrometry and discriminated via isomer-selective photochemistry. Our findings provide a fundamental framework to explore the preparation of inorganic, isovalent species of benzene (C6H6) by formally replacing the C─H moieties alternatingly through phosphorus and nitrogen atoms, thus advancing our perception of the chemical bonding of phosphorus systems.More than 1050 clinical trials are registered at FDA.gov that explore multipotent mesenchymal stromal cells (MSCs) for nearly every clinical application imaginable, including neurodegenerative and cardiac disorders, perianal fistulas, graft-versus-host disease, COVID-19, and cancer. Several companies have or are in the process of commercializing MSC-based therapies. However, most of the clinical-stage MSC therapies have been unable to meet primary efficacy end points. The innate therapeutic functions of MSCs administered to humans are not as robust as demonstrated in preclinical studies, and in general, the translation of cell-based therapy is impaired by a myriad of steps that introduce heterogeneity. In this review, we discuss the major clinical challenges with MSC therapies, the details of these challenges, and the potential bioengineering approaches that leverage the unique biology of MSCs to overcome the challenges and achieve more potent and versatile therapies.Engineered heterostructures formed by complex oxide materials are a rich source of emergent phenomena and technological applications. In the quest for new functionality, a vastly unexplored avenue is interfacing oxide perovskites with materials having dissimilar crystallochemical properties. Here, we propose a unique class of heterointerfaces based on nitride antiperovskite and oxide perovskite materials as a previously unidentified direction for materials design. We demonstrate the fabrication of atomically sharp interfaces between nitride antiperovskite Mn3GaN and oxide perovskites (La0.3Sr0.7)(Al0.65Ta0.35)O3 and SrTiO3. Using atomic-resolution imaging/spectroscopic techniques and first-principles calculations, we determine the atomic-scale structure, composition, and bonding at the interface. The epitaxial antiperovskite/perovskite heterointerface is mediated by a coherent interfacial monolayer that interpolates between the two antistructures. We anticipate our results to be an important step for the development of functional antiperovskite/perovskite heterostructures, combining their unique characteristics such as topological properties for ultralow-power applications.Intrinsically disordered proteins (IDPs) can be degraded in a ubiquitin-independent process by the 20S proteasome. Decline in 20S activity characterizes neurodegenerative diseases. Here, we examine 20S degradation of IDP tau, a protein that aggregates into insoluble deposits in Alzheimer's disease. We show that cleavage of tau by the 20S proteasome is most efficient within the aggregation-prone repeat region of tau and generates both short, aggregation-deficient peptides and two long fragments containing residues 1 to 251 and 1 to 218. Phosphorylation of tau by the non-proline-directed Ca2+/calmodulin-dependent protein kinase II inhibits degradation by the 20S proteasome. Phosphorylation of tau by GSK3β, a major proline-directed tau kinase, modulates tau degradation kinetics in a residue-specific manner. https://www.selleckchem.com/products/hpk1-in-2.html The study provides detailed insights into the degradation products of tau generated by the 20S proteasome, the residue specificity of degradation, single-residue degradation kinetics, and their regulation by posttranslational modification.Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here, we developed a computational framework to identify putative causal noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as putative causal mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation profiles can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.Engineered extracellular vesicles (EVs) carrying therapeutic molecules are promising candidates for disease therapies. Yet, engineering EVs with optimal functions is a challenge that requires careful selection of functionally specific vesicles and a proper engineering strategy. Here, we constructed chimeric apoptotic bodies (cABs) for on-demand inflammation modulation by combining pure membrane from apoptotic bodies (ABs) as a bioconjugation/regulation module and mesoporous silica nanoparticles (MSNs) as a carrier module. MSNs were preloaded with anti-inflammatory agents (microRNA-21 or curcumin) and modified with stimuli-responsive molecules to achieve accurate cargo release at designated locations. The resulting cABs actively target macrophages in the inflammatory region and effectively promote M2 polarization of these macrophages to modulate inflammation due to the synergistic regulatory effects of AB membranes and the intracellular release of preloaded cargos. This work provides strategies to arbitrarily engineer modular EVs that integrate the advantages of natural EVs and synthetic materials for various applications.We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.